Abstract. The pioneering use of Artificial Intelligence (AI) in various fields and sectors, and the growing ethical debate about its application have led research centers, public and private institutions to establish ethical guidelines for a trustworthy implementation of these powerful algorithms. Despite the recognized definition of ethical principles for a responsible or trustworthy use of AI, there is a lack of a sector-specific perspective that highlights the ethical risks and opportunities for different areas of application, especially in the field of Cultural Heritage (CH). In fact, there is still a lack of formal frameworks that evaluate the algorithms’ adherence to the ethical standards set by the European Union for the use of AI in protecting CH and its inherent value. Because of this, it is necessary to investigate a different sectoral viewpoint to supplement the widely used horizontal approach. This paper represents a first attempt to design an ethical framework to embody AI in CH conservation practises to assess various risks arising from the use of AI in the field of CH. The contribution presents a synthesis of the different AI applications to improve the preservation process of CH. It explores and analyses in depth the ethical challenges and opportunities presented by the use of AI to improve CH preservation. In addition, the study aims to design an ethical framework of principles to assess the application of this ground-breaking technology at CH.


/pdf/artificial-intelligence-and-cultural-heritage-design-and-3a2gw3i4.pdf

Artificial intelligence and cultural heritage: design and assessment of an ethical framework

&lt;p&gt;Recent trends in the modus operandi of technologically-aware criminal groups engaged in illicit goods trafficking (e.g., firearms, drugs, cultural artifacts, etc.) have given rise to significant security challenges. The use of cryptocurrency-based payments, 3D printing, social media and/or the Dark Web by organized crime leads to transactions beyond the reach of authorities, thus opening up new business opportunities to criminal actors at the expense of the greater societal good and the rule of law. As a result, a lot of scientific effort has been expended on handling these challenges, with Artificial Intelligence (AI) at the forefront of this quest; mostly machine learning and data mining methods that can automate large-scale information analysis. Deep Neural Networks (DNNs) and graph analytics have been employed for automatically monitoring and analyzing the digital activities of large criminal networks in a data-driven manner. However, such practices unavoidably give rise to ethical and legal issues, which need to be properly considered and mitigated. This paper is the first one to systematically explore all of the above aspects jointly and from a combined perspective, without focusing on a particular angle or type of illicit goods trafficking. It emphasizes how advances in AI both allow the authorities to unravel technologically-aware trafficking networks and provide countermeasures against any potential violations of citizen rights in the name of security.&lt;/p&gt; 

The invisible arms race: digital trends in illicit goods trafficking and AI-enabled responses

Abstract. The use of artificial intelligence (AI) has the potential to be highly effective in detecting and monitoring illegal trafficking of cultural heritage (CH) goods through image classification techniques, particularly on online marketplaces where the trade of stolen CH objects has become a major global issue. Traditional investigation methods are no longer adequate, but with the assistance of AI, law enforcement agencies and CH organizations can now boost monitoring capabilities to detect, track, and possibly recover stolen objects more efficiently. AI algorithms can indeed analyze images to identify unique features and characteristics that can be used to determine their authenticity and provenance. Additionally, AI can detect patterns and networks of illicit trafficking, and link stolen objects to their places of origin, facilitating the recovery process. In this context, the SIGNIFICANCE project (Stop Illicit Heritage Trafficking with Artificial Intelligence) has been specifically designed to increase the response capabilities of public authorities and police corps against the illicit trafficking of cultural goods perpetrated through internet channels (i.e., social platforms, web, and dark web). By leveraging the power of Deep Learning (DL), AI can help prevent the loss of invaluable cultural artifacts and ensure that they are returned to their rightful owners and places of origin. This paper presents the results reached by the SIGNIFICANCE AI framework on image datasets collected over the web and social media through crawling algorithms.


https://doi.org/10.5194/isprs-archives-xlviii-m-2-2023-3-2023

Artificial intelligence to fight illicit trafficking of cultural property

Land consolidation in rural areas is greatly influenced by a number of social, agricultural, and economic factors. They extend the time and complicate activities related to the implementation of this project. Land consolidation is a huge investment, requiring a good and up-to-date map. Up-to-date data that reflects reality are the basis for good and efficient land consolidation in rural areas. To improve the creation of a land consolidation project, the most appropriate solution would be to place all the necessary data to create a land consolidation project on “geoportals” websites. Providing data on the websites of geoportals in Poland and Finland in the form of a map portal would enable both visual and substantive analysis of spatial changes in rural areas. Therefore, this article pursues two main scientific goals. The first purpose of this article is to analyze the history and scope of land consolidation in Poland and Finland in order to learn the specifics of land consolidation in a given country. Comparisons and analyses were made on the basis of the authors’ experience in Poland and Finland with land consolidation in the areas described. The second scientific goal of the article is to compare the availability of geospatial data in the context of the methodology of performing land consolidation in rural areas, which leads to increased energy efficiency in agricultural production by reducing the need for energy, especially fuel for agricultural machinery. The results from the table with a list of geoportals in Poland and Finland are a summary of the data available for free. Thanks to it, land consolidation contractors around the world will have the opportunity to familiarize themselves with and perhaps use this data when creating a rural land consolidation project. Good land consolidation planning will lead to effective and economical use of rural space. This is part of the 2030/2050 strategy. Reducing agricultural production costs, lower fuel consumption, and a smaller carbon footprint. This is what Poland and Finland are striving for as countries belonging to the European Union.

Energy Efficiency in Agricultural Production—Experience from Land Consolidation in Poland and Finland

Abstract. In recent years, the demand for flexible and sustainable strategies in digitization processes has represented a significant challenge for the heritage documentation research community. In particular, the tasks of parametric modelling and AI-based semantic enrichment operations, necessary but traditionally time-consuming, is extremely onerous from a user-oriented perspective. Many efforts of the research community have been dedicated to enhancing efficiency through automation, and one of the possible solutions is represented by the employment of machine learning strategies. This study introduces an innovative methodology that integrates Visual Programming Language platforms and 3D Python libraries, thereby implementing the Scan-to-BIM approach. Two case studies - characterized by varying scales, resolutions, and accuracies - have been analysed to validate the proposed pipeline, demonstrating its flexibility and scalability across architectural objects and archaeological assets belonging to museum collections. The workflow involves several steps, starting from classified 3D and 2D data segmented using machine learning techniques with the aim of managing semantically enriched reality-based data in BIM/HBIM environment without scarifying accuracy criteria. Results highlight the methodology's efficiency and adaptability in diverse contexts, offering a compelling alternative to labour-intensive Scan-to-BIM processes. Ultimately, this methodology contributes to the automation in cultural heritage digitisation, underlining the need for comprehensive standards and protocols in this dynamic domain.


A scalable approach for automating scan-to-bim processes in the heritage field

Abstract. The inability to prevent or eliminate illicit trafficking of cultural goods is not limited to failed-state environments or any specific part of the globe. While the antiquities market denies that this illicit trade is a widespread phenomenon, the international community and Law Enforcement Agencies (LEAs) overwhelmingly recognize the problem indicating that organized crime is involved at all stages. Nowadays, web platforms play host to groups dedicated to illegal archaeological excavations and Illicit trade of cultural goods. Looters have the freedom to connect online with potential buyers around the world. At the same time, social media platform monitoring in search of criminal activities conducted by LEAs is poor due to the lack of expertise, efficient tools to scan the massive amounts of data, and funds. The COVID-19 crisis has compounded the problem by driving more and more dealers and buyers online – where they are discovering that by joining certain unmonitored groups, they can enter the illegal market with ease. The EU funded SIGNIFICANCE project (Stop Illicit heritaGe traffickiNg wIth artiFICiAl iNtelligenCE) has been designed to boost LEAs investigation capabilities in monitoring online illegal activities on social media platforms, the web and the dark web for the identification of cultural property crimes, exploiting Artificial Intelligence and Deep Learning algorithms for guaranteeing the successful prosecution of perpetrators unveiling criminal networks.


/pdf/significance-stop-illicit-heritage-trafficking-with-21a8b3zl.pdf

Significance. stop illicit heritage trafficking with artificial intelligence

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Significance. stop illicit heritage trafficking with artificial intelligence

Artificial intelligence to fight illicit trafficking of cultural property